Learning Classifier Systems (LCSs) are proposed as the universal learning system, and could therefore be used in a wide range of applications. They represent powerful linguistically interpretable rule-based structures, combined with versatile self-adaptive algorithms. However, in standard form, they lack interfaces to continuously varying inputs and outputs. Combining them with Fuzzy Logic solves this problem, but leads to other internal difficulties for the learning system. This article presents an experimental comparison of the two main types of LCS applied to mobile robot control, and suggests some underlying reasons for the remaining learning difficulties.